Application of differential evolution algorithm and comparing its performance with literature to predict rock brittleness for excavatability

Saffet Yagiz, Aitolkyn Yazitova, Halil Karahan

Research output: Contribution to journalArticle

Abstract

The aim of this study was to estimate brittleness of intact rock by applying differential evolution (DE) algorithm and then to compare the results obtained from the optimum model with literature. For this aim, several models including linear and nonlinear were developed for predicting the brittleness via DE algorithm using the dataset obtained from 48 tunnel cases around the world. Each model were developed using 80% of the dataset as training and 20% of the dataset as testing in random. After that, developed models are compared according to the coefficient of correlations (r2), computer process unit (CPU), mean-squared error (MSE) and number of function evaluation (NFE) values to choose the best accurate one among them. It is found that the values r2, MSE, NFE and CPU ranged between 0.9385–0.9501, 8.2616–9.938, 7217–11,176 and 4.91–36.22, respectively, with the quadratic model (QM) indicating the best performance. It is concluded that the DE algorithm is itself very powerful tool for estimating the brittleness; however, the QM is superior especially for simulations in which computational time and optimisation is a critical.

Original languageEnglish
JournalInternational Journal of Mining, Reclamation and Environment
DOIs
Publication statusAccepted/In press - Jan 1 2020

Fingerprint

Brittleness
Rocks
rock
Function evaluation
Chemical reactions
Differential evolution
Tunnels
tunnel
Testing
simulation

Keywords

  • Brittleness
  • differential evaluation
  • excavatability
  • rock tests

ASJC Scopus subject areas

  • Geotechnical Engineering and Engineering Geology
  • Geology
  • Earth-Surface Processes
  • Management of Technology and Innovation

Cite this

@article{78913f6413cb4e3a956a9247c0483fad,
title = "Application of differential evolution algorithm and comparing its performance with literature to predict rock brittleness for excavatability",
abstract = "The aim of this study was to estimate brittleness of intact rock by applying differential evolution (DE) algorithm and then to compare the results obtained from the optimum model with literature. For this aim, several models including linear and nonlinear were developed for predicting the brittleness via DE algorithm using the dataset obtained from 48 tunnel cases around the world. Each model were developed using 80{\%} of the dataset as training and 20{\%} of the dataset as testing in random. After that, developed models are compared according to the coefficient of correlations (r2), computer process unit (CPU), mean-squared error (MSE) and number of function evaluation (NFE) values to choose the best accurate one among them. It is found that the values r2, MSE, NFE and CPU ranged between 0.9385–0.9501, 8.2616–9.938, 7217–11,176 and 4.91–36.22, respectively, with the quadratic model (QM) indicating the best performance. It is concluded that the DE algorithm is itself very powerful tool for estimating the brittleness; however, the QM is superior especially for simulations in which computational time and optimisation is a critical.",
keywords = "Brittleness, differential evaluation, excavatability, rock tests",
author = "Saffet Yagiz and Aitolkyn Yazitova and Halil Karahan",
year = "2020",
month = "1",
day = "1",
doi = "10.1080/17480930.2019.1709012",
language = "English",
journal = "International Journal of Mining, Reclamation and Environment",
issn = "1748-0930",
publisher = "Taylor and Francis",

}

TY - JOUR

T1 - Application of differential evolution algorithm and comparing its performance with literature to predict rock brittleness for excavatability

AU - Yagiz, Saffet

AU - Yazitova, Aitolkyn

AU - Karahan, Halil

PY - 2020/1/1

Y1 - 2020/1/1

N2 - The aim of this study was to estimate brittleness of intact rock by applying differential evolution (DE) algorithm and then to compare the results obtained from the optimum model with literature. For this aim, several models including linear and nonlinear were developed for predicting the brittleness via DE algorithm using the dataset obtained from 48 tunnel cases around the world. Each model were developed using 80% of the dataset as training and 20% of the dataset as testing in random. After that, developed models are compared according to the coefficient of correlations (r2), computer process unit (CPU), mean-squared error (MSE) and number of function evaluation (NFE) values to choose the best accurate one among them. It is found that the values r2, MSE, NFE and CPU ranged between 0.9385–0.9501, 8.2616–9.938, 7217–11,176 and 4.91–36.22, respectively, with the quadratic model (QM) indicating the best performance. It is concluded that the DE algorithm is itself very powerful tool for estimating the brittleness; however, the QM is superior especially for simulations in which computational time and optimisation is a critical.

AB - The aim of this study was to estimate brittleness of intact rock by applying differential evolution (DE) algorithm and then to compare the results obtained from the optimum model with literature. For this aim, several models including linear and nonlinear were developed for predicting the brittleness via DE algorithm using the dataset obtained from 48 tunnel cases around the world. Each model were developed using 80% of the dataset as training and 20% of the dataset as testing in random. After that, developed models are compared according to the coefficient of correlations (r2), computer process unit (CPU), mean-squared error (MSE) and number of function evaluation (NFE) values to choose the best accurate one among them. It is found that the values r2, MSE, NFE and CPU ranged between 0.9385–0.9501, 8.2616–9.938, 7217–11,176 and 4.91–36.22, respectively, with the quadratic model (QM) indicating the best performance. It is concluded that the DE algorithm is itself very powerful tool for estimating the brittleness; however, the QM is superior especially for simulations in which computational time and optimisation is a critical.

KW - Brittleness

KW - differential evaluation

KW - excavatability

KW - rock tests

UR - http://www.scopus.com/inward/record.url?scp=85077867385&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85077867385&partnerID=8YFLogxK

U2 - 10.1080/17480930.2019.1709012

DO - 10.1080/17480930.2019.1709012

M3 - Article

AN - SCOPUS:85077867385

JO - International Journal of Mining, Reclamation and Environment

JF - International Journal of Mining, Reclamation and Environment

SN - 1748-0930

ER -